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Thursday

  1. A6 a6-047 · Baseten Engineering Blog

    Baseten Engineering Blog: A Roundup of Model Performance Optimization Practices

    The model performance section of the Baseten engineering blog rounds up several inference optimization efforts, including speeding up pyannote's speaker diarization model by 9.6x, speeding up Kimi K3 tokenization by 18x under million-token agent workloads, and performing inference engineering optimization for DeepSeek V4 Pro 0813. The section also covers delta weight synchronization optimization for hosted RL rollouts, Coval voice AI benchmarking, and the practice of building APIs for Kimi K3 and GLM-5.2.

    The model performance section of the Baseten engineering blog rounds up several inference optimization efforts, including speeding up pyannote's speaker diarization model by 9.6x, speeding up Kimi K3 tokenization by 18x under million-token agent workloads, and performing inference engineering optimization for DeepSeek V4 Pro 0813. The section also covers delta weight synchronization optimization for hosted RL rollouts, Coval voice AI benchmarking, and the practice of building APIs for Kimi K3 an

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  2. A6 a6-047 · Baseten Engineering Blog

    Baseten Engineering Blog AI Models Article List

    The AI Models section of the Baseten Engineering Blog brings together multiple articles on models and inference engineering, including NVIDIA Nemotron 3 Diarization, which achieves real-time speaker labeling at one cent per audio hour; DeepSeek-V4.1-Flash, which offers more efficient prefill for coding agents; and introductions to new models such as NVIDIA Nemotron 3.5 Lightning, Nemotron 3.5 ASR Streaming, and Nemotron 3 Embed. It also covers topics such as GLM-5.3 post-training scaling, GLM 5.2 vision capabilities, Laguna S 2.1, and recommendations for open-source post-trained models.

    The AI Models section of the Baseten Engineering Blog brings together multiple articles on models and inference engineering, including NVIDIA Nemotron 3 Diarization, which achieves real-time speaker labeling at one cent per audio hour; DeepSeek-V4.1-Flash, which offers more efficient prefill for coding agents; and introductions to new models such as NVIDIA Nemotron 3.5 Lightning, Nemotron 3.5 ASR Streaming, and Nemotron 3 Embed. It also covers topics such as GLM-5.3 post-training scaling, GLM 5.

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    #a6-editorial#English text is being prepared.
  3. A6 a6-047 · Baseten Engineering Blog

    List of Baseten Engineering Blog Infrastructure Articles

    The Baseten Engineering Blog infrastructure section aggregates multiple articles, covering topics such as LLM inference observability, a comparison of H100, H200, and B200 GPUs, building RBAC for enterprises, accelerating cold starts with the Baseten Delivery Network (BDN), running AI video on Nebius, and delivering healthcare GenAI solutions in partnership with Vultr.

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  4. A6 a6-047 · Baseten Engineering Blog

    Baseten Engineering Blog Community Articles Roundup

    The Baseten Engineering Blog Community section brings together multiple articles, covering topics such as open-source inference for You.com, AI model selection lessons from Notion and Gamma, inference in the era of continual learning, model fine-tuning and a conversation with Hollywood, the Baseten inference stack at NVIDIA Dynamo Day, and building agents on Baseten with LangChain Agent Builder + GLM 4.7.

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  11. Latent Space

    OpenAI Team Responds to Computer Use Progress Doubts, Recaps One-Week Sprint on Decisions API

    In a podcast after OpenAI DevDay, Ari Weinstein, who is in charge of the Computer Use agent, responded to Dwarkesh's doubts about Computer Use progressing slowly, saying it is now completely different from a few months ago because the model has become better at debugging and retrying, and can write JavaScript to execute multiple steps at once while combining screenshots, accessibility, DOM, and Playwright. He also mentioned that Dots provides each Dot with a cloud Linux virtual computer, and that Computer Use has entered the Agents API. Nikunj Handa, head of the OpenAI API team, introduced updates to GPT-6 such as asynchronous tool calling, mid-turn steering, WebSockets, UltraFast inference, prompt caching and pre-warming, and compaction, and said the Decisions API is based on Luna weights, with no new model trained, and took only about a week from the emergence of the idea to a prototype.

    In a podcast after OpenAI DevDay, Ari Weinstein, who is in charge of the Computer Use agent, responded to Dwarkesh's doubts about Computer Use progressing slowly, saying it is now completely different from a few months ago because the model has become better at debugging and retrying, and can write JavaScript to execute multiple steps at once while combining screenshots, accessibility, DOM, and Playwright. He also mentioned that Dots provides each Dot with a cloud Linux virtual computer, and tha

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    #newsletter#Industry news#Agents#OpenAI

Wednesday